Abstract
Abstract. For the detection of climate change, not only the magnitude of a trend signal is of significance. An essential issue is the time period required by the trend to be detectable in the first place. An illustrative measure for this is time of emergence (ToE), that is, the point in time when a signal finally emerges from the background noise of natural variability. We investigate the ToE of trend signals in different biogeochemical and physical surface variables utilizing a multi-model ensemble comprising simulations of 17 Earth system models (ESMs). We find that signals in ocean biogeochemical variables emerge on much shorter timescales than the physical variable sea surface temperature (SST). The ToE patterns of pCO2 and pH are spatially very similar to DIC (dissolved inorganic carbon), yet the trends emerge much faster – after roughly 12 yr for the majority of the global ocean area, compared to between 10 and 30 yr for DIC. ToE of 45–90 yr are even larger for SST. In general, the background noise is of higher importance in determining ToE than the strength of the trend signal. In areas with high natural variability, even strong trends both in the physical climate and carbon cycle system are masked by variability over decadal timescales. In contrast to the trend, natural variability is affected by the seasonal cycle. This has important implications for observations, since it implies that intra-annual variability could question the representativeness of irregularly sampled seasonal measurements for the entire year and, thus, the interpretation of observed trends.
Highlights
Since the beginning of the industrialization, the climate system has undergone substantial changes
We find that trend signals in the three carbon cycle variables emerge on much shorter timescales than the physical climate variable sea surface temperature (SST)
We investigate the time of emergence (ToE) of trends in the surface ocean carbon cycle utilizing an ensemble of 17 state-of-the-art Earth system models (ESMs)
Summary
Since the beginning of the industrialization, the climate system has undergone substantial changes. Responsible for these changes is the CO2 emitted by mankind through combustion of fossil fuels, land-use change and industrial processes (e.g., Hegerl et al, 2007), which have brought the global carbon cycle out of steady state. An important issue is the presence of internal variability, which has the potential to enhance or mask forced trends in the atmosphere, land, or ocean (e.g., Latif et al, 1997; Raible et al, 2005; Frölicher et al, 2009; Dolman et al, 2010; Keller et al, 2012). Models are often the only possibility to investigate trends and variability on respective temporal and spatial scales
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